Defining plant ecological specialists and generalists: Building a framework for identification and classification

Abstract Specialization is a widespread but highly ambiguous and context‐dependent ecological concept. This quality makes comparisons across related studies difficult and makes associated terms such as “specialist” and “generalist” scientifically obscure. Here, we present a metric‐based framework to quantify specialization in 141 Quercus species using functional traits, biogeography, and species interactions. Rankings of specialization based on five metrics were used to answer questions about how specialization is used colloquially (i.e., individual species assessment by experts) and influenced by phylogenetics (Ancestral Character State Reconstruction, Automatic Shift Detection), biogeography (patterns of clustering by region and with climate), and species threat level (IUCN Red List). Metric‐based ranking can be representative of specialization in a consistent and practical manner, correlating with IUCN Red List data, and the mean scores of individual expert assessments. Specialization is shown to be highly correlated with precipitation seasonality and only moderately influenced by evolutionary history. Data‐deficient species were more likely to be highly specialized, and higher specialization was positively correlated with greater IUCN threat level. Frameworks for characterizing specialization and generalization can be done using metric ranking and can turn concepts that are often unclear into a definitive system. Metric‐based rankings of specialization can also be used to reveal interesting insights about a clade's evolutionary history and geographic distribution when paired with the related phylogenetic and geographic data. Metric‐based rankings can be applied to other systems and be a valuable tool for identifying species at risk and in need of conservation.


| INTRODUC TI ON
Ecological specialization is a widespread concept in biological fields (Table 1; Berenbaum, 1996). This concept is valuable because specialist species have been shown to have unique characters and be at greater risk of extinction, making their identification critical to conservation efforts (Colles et al., 2009;Devictor et al., 2010;Dudley et al., 2019;Poisot et al., 2011). Some systems show no correlations between specialization and threat to a species, but this is often cited as a failure of characterizing specialization due to the complexity of the concept (Vázquez & Simberloff, 2001). Relevant terminology has become problematic due to ambiguity and variance, making comparison across studies difficult, and the ideas highly context dependent (Devictor et al., 2010). In certain contexts where specialization is clearly defined, it is often reduced to a single concept or metric, limiting designations to a single dimension (Aguilar-Romero et al., 2017;Londero et al., 2017). Defining specialization more broadly can better represent a species' overall ecological strategy and may lead to better correlations with scientific opinion and species threat level, though limitations of comparisons between systems will still exist.
Other studies have shown that standardizing terminology and definitions is of value (Avolio et al., 2019; Table 2).
Attempts to define and classify specialization are numerous and add to the increasing number of definitions assigned to the concept (Ferry-Graham et al., 2002). Studies also often do not make the biological level being referenced clear. Species may be considered specialists relative to their clade, but generalists compared to other genera (Devictor et al., 2010). Issues are further compounded by the fact that many of these studies are purely theoretical and do not attempt to apply concepts to a specific system. This leads to questions such as do experts assess generalized and specialized species in a consistent manner? If they do, can we infer which traits' experts are utilizing to make these designations? The factors that are cited as determining specialization and generalization are often consistent across related literature when they are mentioned ( Table 1).These factors are often clear to see when looking at unambiguously specialist species (Table 2).
Members are carnivorous, herbaceous plants that are endemic to bog-like conditions, and their specialized traits grant them robust fitness in these nutrient-poor, acidic environments (Thum, 1989).
Drosera spp. leaves are also unambiguously specialized, having been modified into an adhesive snare that immobilizes prey for digestion (Thum, 1989). Unambiguous specialists such as these are valuable for characterizing specialization, as their extreme state across multiple characters makes what factors influence the designation clear; narrow habitat preference, highly modified anatomy with potentially narrow use, and a high dependence on other members of the same ecological network. They also demonstrate how a species can become restricted to its evolutionary peak, becoming so specialized that evolution into new environmental niches becomes difficult (Wright, 1932).
Problems arise when assigning specialist and generalist designations without making the biological context clear, and when considering species that are not blatantly specialized. Ambiguous specialists are exactly that; species that could be considered either a specialist or generalist, depending on how you frame your justification ( Table 2). Consider Asarum canadense (wild ginger), which occupies many kinds of upland forests but generally never occurs in habitats that would not be considered forests. Is wild ginger a specialist, highly evolved for upland forests, or a generalist that can inhabit multiple habitats with forest conditions? We cannot say without context. This species may have a narrow set of ecological conditions to utilize relative to its clade, but might seem quite far reaching compared to other genera. In reality, it could be both, and it is more pragmatic to consider specialization and generalization as a spectrum where most species fall somewhere in the middle. We propose a system for characterizing specialization within a clade, where specialization and generalization form a spectrum, rather than act as binary designations, sensu Ainsworth and Drake (2020). We demonstrate the system's utility by examining ecological specialization in the Quercus clade, allowing us to answer questions surrounding specialization: (1) Does colloquial usage of

| Comparisons to Grime's CSR triangle
One system of classifying ecological strategies is Grime's Competition-Stress-Ruderal triangle (Grime, 1977). Each axis of the triangle represents an area where species are forced into tradeoffs, and species that fall near the tips are exhibiting the extreme form of a strategy. Calculating ecological strategies using functional leaf trait data in conjunction with the CSR framework has been shown to be both feasible and of value to related fields, even at a global scale (Pierce et al., 2017). The spectrum of strategies formed by Grime's triangle has some similarities to the spectrum formed between specialization and generalization. Species can be placed on a CSR triangle surface relative to others to estimate how adapted they are for certain strategies. The area a species covers would convey how specialized their traits are to facilitate a strategy (sensu Pierce et al., 2017).
The main problems with using the CSR framework to categorize specialization are threefold; (1) the categories do not directly translate into a linear continuum of specialization to generalization, (2) species may differ in ecological strategy but still be placed on the same region of the CSR triangle, and (3) multidimensionality makes analyses impractical. Co-occurring species in a high-stress environment may not share the same ecological strategy-one may be specialized to conserve captured resources while the other may be a generalist with high plasticity (Emery et al., 1994). It may be that rather than being directly comparable, specialization into a given habitat drives ecological strategy (Dayrell et al., 2018), where tradeoffs are a direct consequence of adaptive specialization (Agrawal, 2019). On a CSR triangle, a specialist's ecological niche should be narrow (a point) while a generalist would have a range of breadth (with measurable area). Furthermore, by conscribing a species by its plasticity and using several metrics to compare to sister species (within a clade), we present a framework that can capture CSR area questions and reduce them to one dimension. While position on the CSR triangle is a three-dimensional metric, our system produces one-dimensional rankings, enabling practical comparative systematics investigations when coupled with phylogenetic data.

| Study system
To demonstrate how a species' degree of specialization can be characterized, genus Quercus is utilized as an ideal system to study specialization (Cavender-Bares, 2019). Quercus is comprised of approximately 455 species of trees and shrubs (Nixon, 1993), with some boasting a wide distribution while others are found only in very narrow ranges Hauser et al., 2017;Kremer & Hipp, 2019;Manos, 2020;Nixon, 2002). Species inhabit regions of extreme climates (drought and high-water availability, and areas with mild to severe winters) and soils .
Quercus species also express a high degree of functional leaf trait

| Ranking process
To objectively assess specialization, we developed a quantitative ranking system comprised of five metrics ( Table 3) similar to traitbased approaches used by Morelli et al. (2019) and Ainsworth and Drake (2020). Traits were chosen through a combination of a priori research and AICc model selection. Species were assigned points toward specialization based on value in each metric compared to all other Quercus species in this study, and scores for each metric were summed to create the final metric-based specialization ranking (Ranking Generation; Figure 1). Not all collected metrics were utilized in this process, some having been omitted due to the model selection process outlined below. Rankings were produced for 141 Quercus species, a large subset of the species included in the maximum likelihood phylogeny of the American oak clade generated in Hipp et al. (2018). Similar metric-based systems could be utilized for most clade level investigations. Rankings were tested against IUCN Red List data (IUCN, 2021) to look for correlation between specialization and threat level. Rankings were similarly compared to the results of a specialization survey, where experts familiar with Quercus were asked to rank the specialization of species (Model Validation; Figure 1).

| Metric-based specialization rankings and percentile scoring
Metric-based specialization rankings are numeric values with higher values representing higher specialization, and lower values representing higher generalization. Depending on where a species' metric value falls within the range for all species for that metric, it is assigned points toward its final specialization ranking. For example, species with small ranges relative to other Quercus members get more points toward specialization. This was repeated for every metric for 141 species. The totals of a species' four metric scores were combined to produce its final ranking, with each representing 25% of the total. Domatia presence would have been the fifth metric, making each represents 20% of the total, but was dropped from the model due to the process outlined in the following section. Species that were data deficient had the weighting of their available metrics adjusted to compensate. For example, a species with three of the four metrics available would have each metric make up 33% of the total instead. This was done to keep data-deficient species on the same scale as fully represented species. How each metric was obtained, the reasoning for its inclusion, and its associated calculations are outlined below.

| Model selection
To independently generate traits for metric-based specialization rankings, an initial literature review and a priori metric selection process were utilized. This yielded more metrics than those included in the final rankings of specialization (Table 3). Akaike information criterion (AICc) was used to compare models for predicting both IUCN TA B L E 3 Ranking metrics, how their relationship to specialization-generalization is interpreted, and the source of the related data Note: All traits below other than those underlined (Domatia presence, leaf venation, and perimeter per unit leaf area) were included in the final AICc selected metric-based rankings model.
Red List designation and average expert survey score. Metrics that appeared in one of the most optimized predictive models for these two datasets were used in ranking generation. All gathered metrics were utilized except for plasticity of perimeter per unit leaf area, plasticity of Leaf Venation, and Domatia presence (  (Pinheiro et al., 2020), and PHYTOOLS v.0.7.80 (Revell, 2012) in comparison with AICc selection outputs from JMP (Version 15.1.0).
The phylogeny utilized for the PGLS analyses was pruned from Hipp et al. (2018), with data-deficient species being dropped from the tree.
The resulting phylogeny had 91 Quercus species at the tips.

| Specialization survey
To create a comparative dataset of Quercus specialization, experts familiar with Quercus species were asked to rank species on level of specialization, and to define specialization and generalization (Supporting Information-Part 1, Survey Sample). Metric-based data were compared to these results to gain insights into consistency of specialization evaluation from experts. These results were also Committee (a diagram is provided in the Supporting Information-Part 1, Figure S1). The top 20th percentile species were assessed as having max EOO scoring due to the logarithmic distribution of values ( Figure 2). These 29 species were assigned zero points toward specialization from EOO; the remaining scores were calculated using Formula 1 (Supporting Information-Part 1).

F I G U R E 1
Flowchart of overall methodology. A priori ranking generation was built on five metrics (later reduced to four due to the model selection process dropping Domatia presence). The leftmost column contains concepts identified as being relevant to specialization in literature, with the next column to the right representing the metrics we used to represent those concepts. Final specialization ranking metrics (determined through model selection) were validated against two control datasets (survey of experts and IUCN red list data). Final specialization rankings were then used to explore phylogenetic and geographic patterns of development and adaptation.

| Number of distinct inhabited ecoregions
Ecoregions are geographic areas where ecosystems and environmental resources are generally similar (Omernik, 1987). III is used throughout this study, as the level of detail is the highest without imbalance between regions. At lower levels, many Quercus species with greatly differing ranges would only inhabit one ecoregion, making it nonfunctional as a differentiating metric. Methods used to define ecoregions are given in Omernik (1995Omernik ( , 2004 and Omernik and Griffith (2014). Here, the number of ecoregions a species occurs is used as a measure of niche breadth and a species' ability to utilize a variety of resources and conditions. Inhabiting a lower number of ecoregions was interpreted as meaning a species is more specialized. Scoring for DEL3 also done using Formula 1 (Supporting Information-Part 1). DEL3 ranged from a high of 59 distinct regions (Q. rubra) to a low of 1 (10 species).

| Plasticity
Trait plasticity is thought to be a useful quality for generalist species to adapt and persist in varied environments-meanwhile, specialized species may lack plasticity at both an individual and evolutionary level (Griffith & Sultan, 2012;Marvier et al., 2004).
The development of plasticity is also thought to be critical for spe- F I G U R E 3 Alignment of metric-based specialization rankings and expert survey score with IUCN red list designations. Tukey-Kramer connecting letters denote differences. 1). Species had an average of 14 ± 12 (SD) individuals, with 115 of 136 species having more than three specimens. Herbarium specimens were standardized to minimize acclimation differences (all leaves were sun grown, and shade grown leaves were avoided).
To partially control for variation that may be abnormal for a species (e.g., hybrids), the functional trait herbarium specimens were validated against oaks grown ex situ (in common gardens and arboretums) and in situ (field observations by experts).
The four functional leaf traits were chosen due to their correlations with leaf economics, hydrologic niche, and/or ecophysiological performance in angiosperms (Ramírez-Valiente et al., 2020).
Petiole length has been shown to influence light capture per unit leaf area (Takenaka, 1994). Leaf length is known to respond to drought and be influenced by environmental pressures (Barre et al., 2015;Deblonde & Ledent, 2001). Leaf lobedness shows the same patterns as leaf length, but additionally has also been shown to be critical as a means to control hydraulic resistance and water balance (Baker-Brosh & Peet, 1997;Sisó et al., 2001). Specific leaf area is tied to ecological niche and highly correlates to foliar nutrient content, which has used as an indicator of plant response to disturbance (Firn et al., 2019;Hoffmann et al., 2005; Ramírez-Valiente et al., 2020).

| Domatia
Anatomical features with narrow utility are a notable aspect of some specialized species. Although Quercus is considered largely generalist in a broader sense, domatia represents specialized anatomy that can be assessed for the oaks. In Quercus, domatia are small chambers made of trichomes at the intersections along the mid-vein of the leaf. These are created to shelter beneficial arthropods that likely help reduce herbivory on the tree. The presence or absence of domatia may be interpreted as being indicative of interspecies specialization.
Domatia presence or absence was assessed for three individuals per species. Each of the three samples was denoted with a 0 (no domatia), 1 (hair present but likely nonfunctional), or 2 (functional domatia present). These were summed per species, and the totals were scored using Formula 1 (Supporting Information-Part 1), but without inverting scores, as higher domatia presence was interpreted as higher specialization.

| Model validation and testing consistency of ecological concepts
To validate the model, associations among the three models of species designation were tested ( Figure 3): Metric-based specialization rankings and IUCN designations, specialization rankings and survey results, and survey results and IUCN designations. This also allowed for the detection of any potential inconsistencies between metricbased rankings, scientific discourse, and conservation efforts.

| Phylogenetic methods
To investigate the relationship between evolutionary history and specialization, metrics related to the concept of specialization were  An Ancestral Character State Reconstruction was also performed using the APE package, to infer ancestral conditions of specialization using our metric-based specialization rankings for modern species, and to further explore evolutionary trends (Irisarri & Zardoya, 2013;

| Geographic methods
Clustering and variance of specialization by ecoregion and the number of distinct species per ecoregion were plotted in ArcMap (10.8.1) and analyzed using a Moran's I (Figures 6, 7). Mean metric-based specialization ranking in each region was determined as the mean specialization value of distinct species that appeared in said region. To assess environmental influence on generalization, PGLS analyses were also performed between metric-based specialization rankings and mean species environmental traits for precipitation seasonality (Bioclimatic model of the NLME package in R, using a maximum likelihood method.

| Correlations between metric-based specialization rankings, specialization survey results, and IUCN red list designations
Metric-based specialization rankings, specialization survey data, and IUCN Red List designations all significantly and positively correlate with one another. A paired t-test between survey data and ranking data reveals that while the two datasets correlate, experts tend to rank species about 14% higher, or more specialized on average (p > t = <.0001, Mean Difference = 13.6, t-Ratio = 7.54). Expert survey responses aligned most with extent of occurrence (p = .0005, R 2 = .10). Survey data and IUCN designations tested significantly with a one-way ANOVA (p = .0089), and a Tukey-Kramer connecting letters report revealed that while species of least concern (LC) differed significantly from those that were threatened (i.e., any designation more severe than Near Threatened, NT), species that were near threatened could not be determined to significantly differ from either the species of least concern or those that were threatened. Compared to species of least concern, experts scored near threatened species as 1% less specialized on average, and threatened species 29.1% more specialized on average. When comparing metric-based specialization rankings and IUCN data, the relationship was also significant, and the connecting letters report exhibited the same pattern (p = .0373); these results are shown graphically in Supporting Information-Part 1, Figures   S4,S5. When compared to species of least concern, near threatened species were ranked as 3.3% more specialized on average, and threatened species were ranked 35.5% more specialized on average. While there was some overlap between the metrics utilized by the IUCN Red Listing process and the metric-based specialization rankings, namely in extent of occurrence, AICc model selection revealed that overlapping metrics were not favored as predictive variables.

| Phylogenetic signals and plasticity correlations
Tests for phylogenetic signal (Blomberg's K and Pagel's λ) yielded significant models for the Metric-based specialization rankings and four of the five metrics used to build the rankings (  Figure S2), or in other words, species that are plastic for one leaf trait are usually plastic for many traits.

| Metric-based specialization rankings, the Quercus phylogeny, and native regions
Within the Quercus phylogeny, there are visible patterns with respect to species metric-based specialization rankings and their native regions ( Figure 4). The most striking pattern is exhibited by the Eastern North American (ENA) clade within Section Quercus spanning Q. prinoides to Q. alba (following descriptions provided by Manos & Hipp, 2021).
This clade contains some of the most generalized species in the study, with rankings ranging from 8.9 (Q. macrocarpa) to 23.0 (Q. michauxii),

| Ancestral character state reconstruction
Across the Quercus phylogeny, there are some clades of highly generalized or specialized species ( Figure 5). The overall pattern in the entire measured clade is one of the moderate generalization, with most ancestors beyond one node being estimated to have a ranking of ~44 (see Supporting Information-Part 1 for additional details about the most generalized and specialized clades).

| Mean metric-based specialization rankings by ecoregion
Mean specialization ranks of ecoregions are significantly clustered (Moran's I, p < .0001; Figure 6), with a pattern of increasing specialization hotspots in regions at lower latitudes and high precipitation seasonality. Examples can be seen in the Southern Florida Coastal Plain, the US west coast, and regions spanning Central America to northern Mexico.

| Specialization and climate
Metric-based specialization scores and the mean precipitation seasonality by species were found to positively associated with no phylogenetic signal (λ = .04, p < .001, Figure 8). As seasonality increases, so do metric-based rankings of specialization. On average, a species with a mean precipitation seasonality of 10 would likely have half the specialization score of a species with a seasonality of 120.

| Viability of metric-based ranking systems
Specialization in Quercus presents interesting insights into specialization as a whole. Creating a practical, objective ranking system of specialization is indeed possible. The ability to assess specialization in bulk could be of great use in studies involving a large number of related taxa (Catenazzi & von May, 2021;Mounce et al., 2018;Reece et al., 2013). Generated rankings provide a good basis of comparison for specialization, and extension threat level, which has utility for conservationists and scientists alike (Reece et al., 2013). Metric-based rankings can be assumed to be accurate designations of specialization, given significant correlations to IUCN Red Listing designations and scientific assessment ( Figure 3). Results indicate that experts are, on average, good at picking out specialized and generalized species, though they tend to rate species as slightly more specialized than a metric-based system would (Figure 3). Correlation between survey responses and extent of occurrence indicate that experts may be using range sizes as a proxy for specialization.
Data-deficient (DD) species tended to have higher specialization rankings. Of the 11 species with only two available metrics to be considered, two scored in the 70s, four in the 80s, and five in the 90s. Results also suggest some correlation between DD and threat level, so the omission of species lacking some metrics is not recommended; of the 11 species ranked highly with missing data, 36% are threatened on the Red List, including the only critically endangered species in this study, Q. boyntonii. Only 10.6% of the 141 species ranked were threatened. Deficiencies in data may be indicative of threat level for a variety of reasons (Todd & Burgman, 1998); species lacking information tend to be less widespread, understudied, and are potentially harder to access. Additionally, species relationships are underreported (McCann, 2007). Limited data on interactions between species may contribute to certain species' higher average ranking of specialization, and by extension, the higher threat level that we observed in data-deficient species. A lack of interactions between species may not necessarily indicate higher or lower specialization; more data are needed to better explore this relationship.
Given the patterns regarding data deficiencies described above, our framework suggests that DD species should be prioritized for conservation, as they are more likely specialized compared to their well-represented relatives. Results in Figure 3 also suggest that scientific literature containing specialists and generalist species (as decided by the authors) may be reliable regarding these designations, even if there is a lack of methodology or biological context provided. Results suggest some unity among scientific designation of specialization, and that there are potentially common characters experts are using in their designations, such as range size. In projects with few species, evaluating specialization could be accomplished through assessment from experts.
However, a metric-based approach may be advantageous when the study system gets increasingly large. Both approaches are potentially useful means of identifying threatened species (Figure 3).

| Evolutionary and geographic patterns of specialization in Quercus
Specialization, and the metrics used herein to represent it, yield insightful phylogenetic and geographic trends. Metrics used to calculate specialization rankings, excepting of the number of interspecies interactions, tested as having phylogenetic signal (Table 4).
Moderate K and λ values indicate a relatively low phylogenetic signal  Table 1). Asterisks* denote significance. Unexpected phylogenetic shifts were only detected for species extent of occurrence (Supporting Information-Part 1, Figure S3).  (Figures 6 and 7). The

F I G U R E 4
Specialization rank and region of Quercus species across Quercus phylogeny (141 species). Major groups are defined at their respective nodes, and the length of the gray bars indicates the relative uncertainty in dating (sensu Hipp et al., 2018). Longer bars at the tips of the tree represent higher specialization (above 50), while short bars indicate more generalized species (below 50). Color of the bar corresponds to a species' native region.
region a species occupies appears to drive overall specialization more so than phylogenetic relationships (sensu Guttová et al., 2019 andManos, 2020). Specialization hotspots are clustered (Moran's I, p < .0001) and support our initial hypothesis that expanding generalist populations radiate into new heterogenous regions, and likely specialize into open niches similar to allopatric speciation.
Regions with high concentrations of specialists have increasingly extreme water availability (both more and less water) compared to ancestral distributions of North American oaks, namely those in ENA, as evidenced by increasing specialization from ENA to Mexico and Central America, and the positive correlation between precipitation seasonality and specialization ranking (Figures 6 and 8). Mexico, Central America, the US West Coast, and the US Southeast all tend to specialize the local oaks more heavily than ENA, likely due to a mix of harsh conditions that challenge more generalized species, namely the extremes of water availability (Ramírez-Valiente et al., 2020). As shown in Figure 8, specialization is significantly correlated with high precipitation seasonality, with no phylogenetic signal. This indicates adaptation in response to environmental conditions, rather than phylogenetically influenced strategies.
Overall, specialization and generalization appear heavily controlled by the geographic region a species is native to, and as such, specialization tends to act as more of an emergent property of a place rather than a more typical inherited trait (Agrawal, 2019;Küttner et al., 2014). This is not too surprising, as specialization falls somewhere between an ecological strategy and a relative F I G U R E 5 Ancestral character state reconstruction of metric-based specialization rank across Quercus phylogeny (91 species). Bars at the tips represent metric-based specialization rank as a distance from 50 (the midpoint of possible rankings). Bars that extend right are species ranked as specialized, while bars extending left are more generalized; bars are color coded by region. The branches of the tree are color coded by the estimated metric-based specialization ranking of ancestors. Despite specialization appearing to be dictated largely by geographic influences, phylogenetic relationships have also played a part in specialization here and in other systems (Cooper & Lenski, 2000).
Within clades and sections of Quercus, trends of specialization often tend to be preserved and clustered (Figure 4). This could be due sympatric species inhabiting similar environments, and therefore adopting similar strategies in response to those environments, rather than closely related species inheriting similar characteristics from ancestral populations ; however, phylogenetic overdispersion within communities may run counter to this pattern . Coexistence between specialists and generalists within a group can be restricted (Egas et al., 2004), which may help to explain species whose metric-based specialization rankings are contrary to their sister taxa.
Overall, Quercus species tend to come from generalized ancestors ( Figure 5). Results of Ancestral Character State Reconstruction suggest that clades of highly generalized or specialized species arise from ancestral oak populations that maintain a moderate level of generalization. Species ranked contrary to their close relatives may have undergone allopatric speciation across varied regions, resulting in environmental pressures that selected for a different strategy (Aguilar-Romero et al., 2017). This may explain large cases of variation in specialization rankings across regions, such as those seen in section Lobatae and section Quercus.
Utilization of this metric-based process could easily be adapted to other systems and may yield useful insights into how F I G U R E 6 Mean metric-based specialization rank of species within each ecoregion across the continental United States through Mexico and Central America.

F I G U R E 7
The number of distinct species inhabiting the ecoregions of north and Central America. Quercus species diversity is highest in the Southeast United States, the United States west coast, and large ranges in Mexico; the number of distinct species is significantly clustered (Moran's I, p < .0001; Figure 7). Species diversity tends to be higher in transitional regions that exist between specialist and generalist dominated regions. These likely contain areas that can accommodate both strategies (e.g., Southeastern Plains). This region spans states bordering the gulf such as Mississippi, Alabama, and Florida, up through much of the US east coast to states like Virginia and North Carolina. Ecoregions in the intermountain west show low species diversity, with most ecoregions containing less than 12 distinct species.

F I G U R E 8
Metric-based specialization is correlated to Quercus species mean precipitation seasonality (bioclimatic variable 15). specialization emerges, and the evolutionary timescale at which it does so. Additional geographic and biological research should also be directed into how water stress seasonality (i.e., BIO15) may drive specialization and what may occur to endemic species under rapid climate change (Hanson & Weltzin, 2000).

ACK N OWLED G M ENTS
We thank colleagues at Morton Arboretum (especially Andrew Hipp) for contributions and insights to this method. We thank all of the experts and International Oak Society members for contributing valuable survey data. We thank the Jim and Kathryn Vonderharr and Radichel Herbarium for their support to this project.

CO N FLI C T S O F I NTE R E S T
The authors have no conflicts of interest to disclose.

O PE N R E S E A RCH BA D G E S
This article has earned an Open Data badge for making publicly available the digitally-shareable data necessary to reproduce the reported results. The data is available at https://github.com/mkapr oth/Querc usSpe ciali zation.

DATA AVA I L A B I L I T Y S TAT E M E N T
Data available through GitHub at https://github.com/mkapr oth/ Q uerc usSpe ciali zation.